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Abstract

Healthcare delivery in African nations has long been a worldwide issue, which is why the United Nations and World Health Organization seek for ways to alleviate this problem and thereby reduce the number of lives that are lost every year due to poor health facilities and inadequate health care administration. Healthcare delivery concerns are most predominant in Nigeria and it became imperatively clear that the system of medical diagnosis must be automated. This paper explores the potential of machine learning technique (decision tree) in development of a malaria diagnostic system. The decision tree algorithm was used in the development of the knowledge base. Microsoft Access and Java programming language were used for database and user interfaces, respectively. During the diagnosis, symptoms are provided by the patient in the diagnostic system and a match is found in the knowledge base.

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2. Literature Review

The concepts of Artificial Intelligence in medicine have been researched upon in several respects. Hong (1988) summarized the potential of AI techniques in medicine as follows:

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Produces new tools to support medical decision making, training and research.

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Integrates activities in medical, computer, cognitive and other sciences, etc.

Early studies in intelligent medical system such as MYCIN, (ASNET, PIP and internist-I have shown to out perfume manual practice of diagnosis in several disease domain (Shortlitte, 1987).

Diagnosis is the identification of abnormal condition that afflicts a specific patient, based on manifested clinical data or lesions. If the final diagnosis agrees with a disease that afflicts a patient, the diagnostic process is correct; otherwise, a misdiagnosis occurred (Feder, 2006). The diagnostic algorithm will be based on disease models stored in the computer knowledge base including the name of disease with the cause, pathogenesis, lesion, pathophysiology, clinical data, syndromes, clinical presentation and complications.

Symptoms, in a strict medical sense, are subjective clues (e.g. pain, nausea) that the patient experiences. These are revealed by the patient during history taking.

Signs are objective clues (e.g. swelling, wheezing) that a clinician detects during steps of physical examination.

Results of test are clues obtained through laboratory test and other techniques (Feder, 2006)

A Decision Tree is a logical model represented as a binary (two-way split) tree that shows how the value of a target variable can be predicated by using the values of a set of predicator variables (Figure 1) (Quinlan, 1986).